Method and system for learning call analysis

ABSTRACT

A system and method are presented for learning call analysis. Audio fingerprinting may be employed to identify audio recordings that answer communications. In one embodiment, the system may generate a fingerprint of a candidate audio stream and compare it against known fingerprints within a database. The system may also search for a speech-like signal to determine if the endpoint contains a known audio recording. If a known audio recording is not encountered, a fingerprint may be computed for the contact and the communication routed to a human for handling. An indication may be made as to if the call is indeed an audio recording. The associated information may be saved and used for future identification purposes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/014,530 filed Aug. 30, 2013, now allowed, entitled “Method and Systemfor Learning Call Analysis,” which claims priority to U.S. provisionalapplication 61/695,039 filed Aug. 30, 2012, also entitled “Method andSystem for Learning Call Analysis,” the contents of which areincorporated herein.

BACKGROUND

The present invention generally relates to telecommunication systems andmethods. More particularly, the present invention pertains to thedetection of recorded audio by automated dialer systems in contactcenters.

SUMMARY

A system and method are presented for learning call analysis. Audiofingerprinting may be employed to identify audio recordings that answercommunications. In one embodiment, the system may generate a fingerprintof a candidate audio stream and compare it against known fingerprintswithin a database. The system may also search for a speech-like signalto determine if the endpoint contains a known audio recording. If aknown audio recording is not encountered, a fingerprint may be computedfor the contact and the communication routed to a human for handling. Anindication may be made as to if the call is indeed an audio recording.The associated information may be saved and used for futureidentification purposes.

In one embodiment, a method is presented for communication learning in atelecommunication system, wherein the telecommunication system comprisesat least an automated dialer, a telephony service module, a database,and a media server operatively coupled over a network for exchange ofdata there between, the method comprising the steps of: selecting, bythe automated dialer, a contact from the database, the contact beingassociated with a telephone number and one or more acousticfingerprints; retrieving, by the telephony service module, from thedatabase, the one or more acoustic fingerprints and the telephone numberassociated with the contact; initiating, by the automated dialer, acommunication with the contact based on the telephone number, thecommunication generating audio; analyzing, by the media server, theaudio for matches to any of the one or more of the acousticfingerprints, wherein matches are not identified; routing, by thetelephony service module, the communication to an agent, wherein theagent determines the communication comprises a speech recording; andrequesting, by the automated dialer, new acoustic fingerprints from themedia server for the speech recording and associating the new acousticfingerprints with the contact in the database.

In another embodiment, a method is presented for communication learningin a telecommunication system, wherein the telecommunication systemcomprises at least an automated dialer, a telephony service module, adatabase, and a media server operatively coupled over a network forexchange of data there between, the method comprising the steps of:selecting, by the automated dialer, one or more contacts from thedatabase; performing a lookup, by the automated dialer, for existingacoustic fingerprints associated with each of the one or more contactsin the database; initiating, by the automated dialer, a communicationwith one of the one or more contacts, the communication generatingaudio; determining, by the media server, whether any of the existingacoustic fingerprints associated with the contact is present in theaudio, wherein matches are not identified; determining, by the mediaserver, a new acoustic fingerprint for the contact; and updating, by theautomated dialer, a record for the contact in the database byassociating the new acoustic fingerprint with the contact in thedatabase.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the basic components of an embodimentof a learning call analysis system.

FIG. 2 is a flowchart illustrating an embodiment of the process of calllearning.

FIG. 3 is a table illustrating an embodiment of an automated dialerrecord.

FIG. 4 is a table illustrating an embodiment of an automated dialerrecord.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the embodiment illustrated inthe drawings and specific language will be used to describe the same. Itwill nevertheless be understood that no limitation of the scope of theinvention is thereby intended. Any alterations and further modificationsin the described embodiments, and any further applications of theprinciples of the invention as described herein are contemplated aswould normally occur to one skilled in the art to which the inventionrelates.

In an embodiment of a contact center scenario, outbound communications,such as phone calls may be made automatically by a class of devicesknown as “automated dialers” or “autodialers”. In another embodiment ofa contact center scenario, outbound communications may also be placedmanually. A number of humans, or agents, may be available to join intocommunications that are determined to reach a live person. When a callis initiated, a determination may be made as to whether the call wasanswered by a live speaker. A contact center may become more efficientby not having agents involved in a communication until it is determinedthat there is a live person at the called end with whom the agent mayspeak.

Answering machine detection (AMD) is critical to contact centersutilizing automated dialer systems because most calls placed oftenresult in pre-recorded audio from a machine or other automated system.Every audio recording or other automated system that is incorrectlydetected as a live speaker may be routed to an agent for handling.Agents may thus begin to assume an audio recording is at the other endof the call and mistakenly hang up on a live person, sound surprised,lose their train of thought, etc. AMD employs various signal processingalgorithms to classify the entity that picks up a communication intocategories, for example, such as answering machines, or recorded audio,and live speakers. The accuracy of these algorithms may depend onvarious parameters and requires trading off AMD rate and live speakerdetection (LSD) rate. For example, biasing an autodialer towards a highAMD rate may result in more live speakers being classified incorrectlyas recorded audio and hung-up on by the autodialer and vice-versa.

Some countries as well as applications, such as high-value dialing forexample, do not allow or utilize AMD because of false positives. Anexample of a false positive may include a live speaker who is classifiedas an answering machine. Thus, AMD may be disabled or tuned heavilytoward LSD. A large number of audio recordings may thus be routed toagents.

In another example, an autodialer operation may contact the same phonenumber multiple times in a day to try and reach a live speaker. If thecalled number connects to an audio recording that call analysis cannotcorrectly detect. For example, the audio recording “Hi <long pause> Wearen't available right now . . . ” may result in the system notdetecting that a human is not speaking. As a result, each time thatnumber is dialed it may be mistakenly routed to an agent. By learningthe fingerprint of a specific audio recording, the autodialer mayprevent an audio recording from being repeatedly routed to an agent. Ifan audio recording associated with a contact is altered, however, thesystem may have to relearn the fingerprint of the audio recording thenext time that number is dialed. This information may be added to therecord of the contact and stored for future use.

A fingerprint, in the field of Acoustic Fingerprinting, may be a passiveunique trait to identify a particular thing. A system may generate afingerprint of a candidate audio stream and compare the newly generatedfingerprint against a database of known fingerprints. The fingerprintsmay be used in communications systems for routing purposes. Humans maywant to increase the opportunity to interact with another human insteadof with an audio recording. Thus, contacts having fingerprintsidentifying audio recordings may not be handled by a human and routedotherwise, for example.

By learning about audio recordings that are missed, learning callanalysis, in one embodiment, allows contact centers using automateddialers to turn down their AMD bias and turn up their LSD bias. Thus,contact centers may be able to maximize the number of live speakers thatare routed to agents. The contact center may become aware that theincreased number of audio recordings that initially come through toagents. The audio recordings may be marked as a recording and maysubsequently not be re-routed to agents when they are recognized.

FIG. 1 is a diagram illustrating the basic components in an embodimentof a learning call analysis system, indicated generally at 100. Thebasic components of a system 100 may include: a telephony servicesmodule 105, which may include a media server 115; an automated dialer110; a network 120; an agent workstation 125, which may include a workstation computer 128 coupled to a display 127, and a telephone 126; adatabase 130; and a call endpoint 135.

The telephony services module 105 may include a media server 115. In oneembodiment, the telephony services module 105 may comprise anapplication programming interface (API) that receives audio recordingfingerprints through the automated dialer 110 and sends the fingerprintsto the media server 115 when placing a call. The media server 115 mayreceive answering audio recording fingerprints and use them as part ofthe call analysis. The media server 115 may also be able to generatefingerprints and send these to the telephony services module 105 whenrequested.

In one embodiment, the automated dialer 110 may comprise a device thatautomatically dials telephone numbers. In another embodiment, theautomated dialer may comprise software. An example may be InteractiveIntelligence, Inc.'s, Interaction Dialer®. In one embodiment, theautomated dialer 110 may have a lookup or caching mechanism that matchesphone numbers, or other contact information, to existing audio recordingfingerprints for communications about to be placed. In one embodiment,when a call is sent to an agent and identified as an audio recording,the automated dialer 110 may request the fingerprints for that call fromthe telephony services 105 and media server 115 and update databasetables.

The network 120 may be in the form of a VoIP, a network/internet basedvoice communication, PTSN, mobile phone network, Local Area Network(LAN), Municipal Area Network (MAN), Wide Area Network (WAN), such asthe Internet, a combination of these, or such other network arrangementas would occur to those skilled in the art. The operating logic ofsystem 100 may be embodied in signals transmitted over network 120, inprogramming instructions, dedicated hardware, or a combination of these.It should be understood that any number of computers 128 can be coupledtogether by network 120.

The agent workstation 125 may include a work station computer 128coupled to a display 127. Workstation computers 128 may be of the sametype, or a heterogeneous combination of different computing devices.Likewise, displays 127 may be of the same type or a heterogeneouscombination of different visual devices. It should be understood thatwhile one work station 125 is described in the illustrative embodiment,more may be utilized. Contact center applications of system 100typically include many more workstations of this type at one or morephysical locations, but only one is illustrated in FIG. 1 to preserveclarity. In another embodiment, agents may not even be utilized, such asin a system that regularly leaves messages, but provides an IVR with amessage and options if a live speaker is encountered. Further it shouldbe understood that while a contact center is mentioned and agents arereferred to, it is within the scope of this material not to limitapplication to a contact center setting.

A digital telephone 126 may be associated with agent workstation 125.Additionally, a digital telephone 126 may be integrated into the agentcomputer 128 and/or implemented in software. It should be understoodthat a digital telephone 126, which is capable of being directlyconnected to network 100, may be in the form of handset, headset, orother arrangement as would occur to those skilled in the art. It shallbe further understood that the connection from the network 120 to anagent workstation 125 can be made first to the associated workstationtelephone, then from the workstation telephone to the workstationcomputer by way of a pass-through connection on the workstationtelephone. Alternatively, two connections from the network can be made,one to the workstation telephone and one to the workstation computer.Although not shown to preserve clarity, an agent workstation 125 mayalso include one or more operator input devices such as a keyboard,mouse, track ball, light pen, tablet, mobile phone and/ormicrotelecommunicator, to name just a few representative examples.Additionally, besides display 127, one or more other output devices maybe included such as loudspeaker(s) and/or a printer.

The database 130 may house the automated dialer 110 records. The recordscontained in the database 130 may enable the system 100 to determinewhether a fingerprint is present. The records may also enable the systemto determine whether an audio recording is present at the other end of acommunication.

In one embodiment, the call endpoint 135 may represent the endpoint of acall placed by the system through the network 120 and there is ananswer. The answer may be by any entity, such as a live speaker or anaudio recording, for example.

As illustrated in FIG. 2, a process 200 for illustrating call learningis provided. The process 200 may be operative in any or all thecomponents of the system 100 (FIG. 1).

In step 205, a contact is selected for communication. For example,telephone numbers and matches are cached. In one embodiment, whentelephone numbers that are to be dialed are cached, the automated dialermay also cache any fingerprint matches that are found within the system.In one embodiment, multiple fingerprints per contact may be stored.Instances of multiple fingerprints may occur when audio recordings playdifferent announcements based on the time of day or the day of the week,for example. Control is passed to operation 210 and the process 200continues.

In operation 210, a fingerprint look up is performed for the selectedcontacts. For example, there may be a fingerprint for any giventelephone number of a contact, such as the fingerprint of the audiorecording that an agent experienced when a call was placed to thatnumber. In some instances, a telephone number may result in more thanone audio recording such as with call forwarding. In at least oneembodiment, fingerprints may be found using the telephone number as areference or via other forms of identification such as a name, customerID, etc. Control is passed to operation 215 and the process 200continues.

In operation 215, a call is placed. For example, the call may beperformed via the telephony services. In one embodiment, the automateddialer may supply the fingerprint in the API call when the call isinitiated. The telephony services may then relay any fingerprintsassociated with the telephone number to the media server that wereidentified in the fingerprint lookup. In at least one embodiment,fingerprints may include those of voice mail systems, answeringmachines, network messages, etc., or any other type of answering serviceor audio recording. Control is passed to operation 220 and the process200 continues.

In operation 220, the media server listens for speech or a fingerprintmatch. In one embodiment, the fingerprints may indicate that the systemhas encountered the same audio recording previously. The fingerprint mayalso indicate that the message has changed in the recording. Control ispassed to step 225 and process 200 continues.

In operation 225, it is determined whether there is a fingerprint match.If it is determined that there is a fingerprint match, then control ispassed to operation 230 and process 200 continues. If it is determinedthat there is not a fingerprint match, then control is passed tooperation 235 and process 200 continues.

The determination in operation 225 may be made based on any suitablecriteria. For example, when the media server encounters a recording thathas a familiar fingerprint, i.e., it has been learned, then the mediaserver may inform telephony services which in turn may inform theautomated dialer that there is a match. If a match for the fingerprintis found, this may indicate an audio recording. However, if there is nomatch, then the fingerprint could indicate a live person or a changedrecording and this will be determined by other means, such as the agent.In one embodiment, the agent may indicate in the record the entity atthe other end of the call.

In operation 230, a record may be inserted for that telephone number.For example, a record may be inserted into FIG. 4 indicating that thetype is “d”, indicating that an audio recording has been detected andthe process ends. In one embodiment, other actions may be performed suchas disconnecting the communication, scheduling another communication tooccur at a later point in time, leaving a message on an answeringmachine, determining an alternate contact to try, and routing thecommunication to a handler.

In operation 235, a fingerprint is computed for the telephone number.For example, a unique identifier may be created for the contact. Controlis passed to step 240 and process 200 continues.

In operation 240, the communication is routed. For example, a call maybe routed to an agent within a contact center. Control is passed to step245 and process 200 continues.

In operation 245, it is determined whether the endpoint of the contacthas been associated with an audio recording. If it is determined thatthe endpoint of the communication is an audio recording, then control ispassed to operation 250 and process 200 continues. If it is determinedthat the endpoint of the call is not an audio recording, then control ispassed to operation 255 and process 200 continues.

The determination in operation 245 may be made based on any suitablecriteria. For example, records within the database may be examined. Inone embodiment, if a live speaker call is dispositioned by an agent witha wrap-up code indicating that an audio recording has been routed to theagent, then the autodialer requests the audio recording fingerprint fromtelephony services/media server and writes it to the database. Theautodialer may then take the telephone number and the correspondingfingerprint and look it up as described in FIG. 3 below. If thecombination of the contact number and fingerprint is found, then theinformation illustrated in FIG. 4 below may be supplemented. In oneembodiment, the indicator “Fingerprint missed” may be input in therecord because call analysis should have detected this call as an audiorecording, but failed to. If the combination is not found, then theautodialer looks up the contact number. If that record is found, thenthe autodialer may overwrite the fingerprint for that contact record.Alternatively, multiple fingerprints may be kept according to rules suchas the maximum age, maximum number, etc. The autodialer may also inserta type into a record (FIG. 4) indicating that the fingerprint haschanged even though the record has been found. For example, in oneembodiment, an audio recording may have changed, such as a personchanging the message on their answering machine. In another embodiment,the fingerprint of the communication may be added to the database evenif a live caller is detected. Storing such information may serve toensure the agents are not skewing their statistics by pretending to talkto a person and letting an answering machine record the conversation.

In operation 250, a record is inserted associating a contact with afingerprint and the process ends. In at least one embodiment, multiplefingerprints may be associated with a number. Multiple fingerprints mayresult in instances where, for example, different audio recording may beplayed based on the time of day or the day of the week.

In operation 255, a record is inserted. For example, if a phone numberis not found, the autodialer may insert a new record into the existingrecord, of which an embodiment is illustrated in FIG. 3, which includesthe ID of the agent that dispositioned the call as an audio recording.The record illustrated in FIG. 4 may also contain information insertedindicating that the communication was a type of “initial detect”, whichmay indicate that an audio recording is being encountered for the firsttime and a fingerprint is being added. Control is passed to operation260 and the process 200 continues.

In operation 260, an agent interacts with the contact, which may be alive person, and the process ends.

FIG. 3 illustrates an embodiment of an autodialer record table,indicated generally at 302. The autodialer record table 302 may becomposed of a number of autodialer records 300. While only record 300 ahas been illustrated in FIG. 3, any number of records 300 may beprovided. An autodialer record 300 may be associated with or resident inthe database 130 (FIG. 1). An autodialer record 300 may include an IDfield 305, a Contact Identifier field 310, a Fingerprint field 315, andan Identified By field 320.

The ID field 305 may contain a record ID, which is necessarily uniqueand preferably used consistently. In at least one embodiment, this fieldmay be a primary key. Using the example shown in FIG. 3, record 300 ahas an ID of 1.

The Contact Identifier field 310 may contain the telephone number of thecontact. This number may have a specified format, such as all digits.Using the example shown in FIG. 3, Record 300 a contains a value of“3175555555” for the telephone number field 310.

The Fingerprint field 315 may contain the fingerprint converted intosome form convenient for storage in the database record as would occurto those skilled in the art. It may be fixed or of a variable length orformat or comprise a reference to external storage, for example.Although particular examples of fingerprints are presented herein, anysort of unique identifier may be used without departing from the scopeof the embodiments herein. Using the example shown in FIG. 3, record 300a contains a fingerprint field 315 value of “RmluZ2VycHJpbnQx”.

The Identified By field 320 may contain information relating to themeans by which the communication was addressed. For example, the fieldmay contain information about how the call was answered. In oneembodiment, this information in the record could include the user ID ofthe agent that identified this fingerprint as an audio recording or itmay indicate that the system identified the fingerprint as an audiorecording. Using the example shown in FIG. 3, record 300 a contains anidentified by field 320 value of “system”, which may indicate that thecommunication was identified by the system as an audio recording.

FIG. 4 is a table illustrating an embodiment of an autodialer recordtable, indicated generally at 402. The autodialer record table 402 maybe composed of a number of autodialer records 400. While only record 400a has been illustrated in FIG. 4, any n umber of records 400 may beprovided. An autodialer record 400 may be associated with or resident inthe database 130 (FIG. 1). An autodialer record 400 may include an IDfield 405, an Insert Date Field 410, and a Type field 415.

The ID field 405 may contain a record ID, which is necessarily uniqueand preferably used consistently. A record ID may indicate an identifierthat is used relevant to each contact account. Although particularexamples of IDs are presented herein, any sort of unique identifier maybe used without department from the scope of the embodiments herein. Inat least one embodiment, this field may be a primary key. Using theexample shown in FIG. 3, record 400 a has an ID of 1.

The Insert Date Field 410 may contain the date of the record. In atleast one embodiment, this information may have a specified format, suchas Aug. 21, 2012. Using the example shown in FIG. 4, record 400 acontains a value of “Aug. 21, 2012” for the insert date field 410.

The type filed 415 may contain information about the call from thesystem. In at least one embodiment, the call may be expressed in values.For example, “i” for an “initial detect”, “d” for “detect”, “c” for“detect/fingerprint changed”, or “m” for “fingerprint missed”. An“initial detect” may describe an audio recording that is beingencountered for the first time. “Detect” may describe that an audiorecording has been detected and the associated fingerprint.“Detect/fingerprint changed” may indicate that an audio recording hasbeen detected but the fingerprint has changed. An example may include anewly recorded message on an answering machine. “Fingerprint missed” mayindicate a combination of a phone number and a fingerprint matching anexisting entry in the table yet the media server did not detect therecording as such. Although particular examples are presented herein,any sort of unique identifier may be used without departing from thescope of the embodiments herein. Using the example shown in FIG. 4,record 400 a contains a value of “c”, which may indicate that an audiorecording has been detected but the fingerprint has changed.

Periodically, a contact center may run a script on the record tables inFIGS. 3 and 4 in the database to remove old contacts that have not beencalled in a specified period of time (e.g., 2 years). Reports may alsobe generated that can identify such information as to how many audiorecordings were not routed to an agent on a given day. Administratorsmay also periodically run reports on agents with high numbers ofassociated fingerprints to see if they are mis-characterizing livespeakers as audio recordings or vice versa.

In at least one embodiment, to avoid routing answering machines orrecorded audio incorrectly classified as a live caller for contactswhere the system does not yet have a fingerprint record of the audiorecording stored in its database, the algorithmic classification intolive caller and audio recording would not be used. Instead, a call tothe new telephone number would be placed. The call analysis system maysearch for a speech-like signal and for fingerprint matches. If there isno matching fingerprint, a fingerprint may be created of the signal andthe call disconnected. A message may optionally be played beforedisconnecting or some other means of handling the call may be employed.The number may be called again at some point in time and if thefingerprint matches, then it can be determined whether the endpoint isan audio recording. If the fingerprint does not match, then the endpointmay be a live caller or an audio recording. If the contact attemptresults in a live caller, the fingerprint may not be stored in thedatabase. Any subsequent contact attempt to that number would want toutilize the same algorithm again (i.e. call first, take the fingerprint,and call back) as it may be that only confirmed audio recordings, suchas where there was a positive fingerprint match in the subsequent callfor example, would be stored in the database. For numbers where thesystem does not yet have a fingerprint of the audio recording, thesystem may employ statistical or heuristic models to determine the timeto place the call where the likelihood of reaching a machine is highestsuch as when the likelihood of reaching a live caller is lowest. Thiscontrasts with contacts where fingerprints are present and the systemwould place communications to maximize the likelihood of reaching a livecaller.

In one embodiment, if a fingerprint match is made between acommunication endpoint and an existing record in the database, one ormore alternate contacts may be used for that record in an attempt toreach a desired endpoint, such as a human. For example, the “FindMe/Follow Me” feature could be employed where several telephone numbersare on record for the same contact. When attempting to locate thecontact, the system places calls to these numbers in some order until itreaches the human. After having learned the fingerprints the first timea particular recording is encountered, the system may subsequently beable to identify numbers where an automated system, such as an answeringmachine or voice mail system, answers without the risk offalse-positives of non-fingerprint based AMD.

In one embodiment, where contact centers want to ensure a live caller isnot incorrectly classified as an audio recording, calls to numbers forwhich there is no fingerprint in the database may be performed with AMDdisabled. Instead, the call analysis may look for the first utterancerepresented as a speech-like signal. A fingerprint may be created of thefirst utterance and returned to the telephony services. The call maythen be routed to an agent. When the agent indicates the call endpointis an audio recording, the fingerprint may be added to the database forthat number. When a number is subsequently called for which an audiorecording fingerprint exists, this is passed to the media server. Ifspeech is encountered that matches any of the fingerprints, then it canbe determined this may be an audio recording, otherwise, everything elseis assumed to be a live caller and the call is passed to agents.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, the same is to be considered asillustrative and not restrictive in character, it being understood thatonly the preferred embodiment has been shown and described and that allequivalents, changes, and modifications that come within the spirit ofthe inventions as described herein and/or by the following claims aredesired to be protected.

Hence, the proper scope of the present invention should be determinedonly by the broadest interpretation of the appended claims so as toencompass all such modifications as well as all relationships equivalentto those illustrated in the drawings and described in the specification.

1. A method for communication learning in a telecommunication system,wherein the telecommunication system comprises at least an automateddialer, a telephony service module, a database, and a media serveroperatively coupled over a network for exchange of data there between,the method comprising the steps of: a. selecting, by the automateddialer, a contact from the database, the contact being associated with atelephone number and one or more acoustic fingerprints; b. retrieving,by the telephony service module, from the database, the one or moreacoustic fingerprints and the telephone number associated with thecontact; c. initiating, by the automated dialer, a communication withthe contact based on the telephone number, the communication generatingaudio; d. analyzing, by the media server, the audio for matches to anyof the one or more of the acoustic fingerprints, wherein matches are notidentified; e. routing, by the telephony service module, thecommunication to an agent, wherein the agent determines thecommunication comprises a speech recording; and f. requesting, by theautomated dialer, new acoustic fingerprints from the media server forthe speech recording and associating the new acoustic fingerprints withthe contact in the database.
 2. The method of claim 1, wherein thetelephony service module comprises an application programming interfacethat receives audio recording fingerprints.
 3. The method of claim 1,wherein step (c) further comprises the step of supplying existingfingerprints associated with a contact to a media server.
 4. The methodof claim 1, wherein each of the one or more acoustic fingerprintscomprises a unique identifier.
 5. The method of claim 1, wherein each ofthe one or more acoustic fingerprints further comprises informationassociating the fingerprints with at least one of: voicemail, answeringmachine, network message, answering service, and audio recording.
 6. Themethod of claim 1, further wherein step (d) further comprisesdetermining that the audio is from a human.
 7. The method of claim 1,wherein step (e) further comprises the agent associating a dispositionof the communication with the contact in the database.
 8. The method ofclaim 6, wherein the disposition comprises a wrap-up code identifyingthe communication as comprising a speech recording.
 9. A method forcommunication learning in a telecommunication system, wherein thetelecommunication system comprises at least an automated dialer, atelephony service module, a database, and a media server operativelycoupled over a network for exchange of data there between, the methodcomprising the steps of: a. selecting, by the automated dialer, one ormore contacts from the database; b. performing a lookup, by theautomated dialer, for existing acoustic fingerprints associated witheach of the one or more contacts in the database; c. initiating, by theautomated dialer, a communication with one of the one or more contacts,the communication generating audio; d. determining, by the media server,whether any of the existing acoustic fingerprints associated with thecontact is present in the audio, wherein matches are not identified; e.determining, by the media server, a new acoustic fingerprint for thecontact; and f. updating, by the automated dialer, a record for thecontact in the database by associating the new acoustic fingerprint withthe contact in the database.
 10. The method of claim 9, wherein thetelephony service module comprises an application programming interfacethat receives audio recording fingerprints.
 11. The method of claim 9,wherein step (c) further comprises the step of supplying existingfingerprints associated with a contact to a media server.
 12. The methodof claim 9, wherein each of the existing acoustic fingerprints comprisesa unique identifier.
 13. The method of claim 9, wherein each existingacoustic fingerprints further comprises information associating thefingerprints with at least one of: voicemail, answering machine, networkmessage, answering service, and audio recording.
 14. The method of claim9, further wherein step (d) further comprises determining that the audiois from a human.
 15. The method of claim 9, wherein the determining ofstep (e) further comprises determining the communication comprises aknown audio recording which is not associated with the contact.